NBA Team Risk Matrix (2023-24 Season)

Mapping team performance uncertainty against win probability

30DayChartChallenge
Data Visualization
R Programming
2025
This NBA risk matrix visualizes team performance uncertainty against win probability for the 2023-24 season. By mapping standard deviation of point differential against win percentage, it reveals which teams are consistently good performers versus those with unpredictable outcomes. The visualization highlights risk quadrants and uses color to indicate average point differential, with circle size representing the likelihood of blowout losses.
Published

April 25, 2025

Figure 1: NBA Team Risk Matrix visualization showing team performance uncertainty vs. win percentage for 2023-24 season. Teams are plotted in four quadrants: high-risk/unpredictable (upper left), good but unpredictable (upper right), consistently poor (lower left), and consistently good (lower right). Teams are color-coded by point differential (red=negative, blue=positive) with circle size representing blowout risk. Dallas shows the highest variability, while Boston has the best performance.

Steps to Create this Graphic

1. Load Packages & Setup

Show code
## 1. LOAD PACKAGES & SETUP ----
suppressPackageStartupMessages({
pacman::p_load(
   tidyverse,      # Easily Install and Load the 'Tidyverse'
  ggtext,         # Improved Text Rendering Support for 'ggplot2'
  showtext,       # Using Fonts More Easily in R Graphs
  janitor,        # Simple Tools for Examining and Cleaning Dirty Data
  skimr,          # Compact and Flexible Summaries of Data
  scales,         # Scale Functions for Visualization
  lubridate,      # Make Dealing with Dates a Little Easier
  ggrepel,        # Automatically Position Non-Overlapping Text Labels with 'ggplot2'
  hoopR,          # Access Men's Basketball Play by Play Data'
  camcorder       # Record Your Plot History
  )
})

### |- figure size ----
gg_record(
    dir    = here::here("temp_plots"),
    device = "png",
    width  = 8,
    height = 8,
    units  = "in",
    dpi    = 320
)

# Source utility functions
suppressMessages(source(here::here("R/utils/fonts.R")))
source(here::here("R/utils/social_icons.R"))
source(here::here("R/utils/image_utils.R"))
source(here::here("R/themes/base_theme.R"))

2. Read in the Data

Show code
# Get game results data for the 2023-24 season
games <- nba_leaguegamefinder(season = "2023-24")
games_data <- games$LeagueGameFinderResults

3. Examine the Data

Show code
glimpse(games_data)
skim(games_data)

4. Tidy Data

Show code
### |- Tidy ----
games_data <- games_data |>  
  mutate(
    PLUS_MINUS = as.numeric(PLUS_MINUS),
    GAME_DATE = as.Date(GAME_DATE),
    PTS = as.numeric(PTS),
    OPP_PTS = as.numeric(PTS) - as.numeric(PLUS_MINUS)
  )

team_variability <- games_data |>
  group_by(TEAM_NAME) |>
  summarize(
    games_played = n(),
    avg_points = mean(as.numeric(PTS)),
    avg_point_diff = mean(PLUS_MINUS),
    points_sd = sd(as.numeric(PTS)),
    margin_sd = sd(PLUS_MINUS),
    win_pct = sum(WL == "W") / n(),
    blowout_risk = sum(PLUS_MINUS <= -10) / games_played,
    team_abbr = first(TEAM_ABBREVIATION),
    .groups = "drop"
  ) |>
  filter(games_played >= 10) |>
  arrange(desc(margin_sd))

5. Visualization Parameters

Show code
### |-  plot aesthetics ----
colors <- get_theme_colors(
  palette = c(
    "#C62828", "#EF6C00", "#B71C1C", "#2E7D32",
    "#D32F2F", "#9E9E9E", "#1976D2"
    )
  )

### |-  titles and caption ----
# text
title_text    <- str_wrap("NBA Team Risk Matrix (2023-24 Season)",
                          width = 60) 

subtitle_text <- str_wrap("Mapping team performance uncertainty against win probability",
                          width = 60)

caption_text <- create_dcc_caption(
  dcc_year = 2025,
  dcc_day = 25,
  source_text =  "ESPN via { hoopR } package" 
)

### |-  fonts ----
setup_fonts()
fonts <- get_font_families()

### |-  plot theme ----

# Start with base theme
base_theme <- create_base_theme(colors)

# Add weekly-specific theme elements
weekly_theme <- extend_weekly_theme(
  base_theme,
  theme(
    
    # Text styling 
    plot.title = element_text(face = "bold", family = fonts$title, size = rel(1.14), margin = margin(b = 10)),
    plot.subtitle = element_text(family = fonts$subtitle, color = colors$text, size = rel(0.78), margin = margin(b = 20)),
    
    # Axis elements
    axis.title = element_text(color = colors$text, size = rel(0.8)),
    axis.text = element_text(color = colors$text, size = rel(0.7)),
    
    axis.line.x = element_line(color = "gray50", linewidth = .2),
    
    # Grid elements
    panel.grid.minor = element_line(color = "gray65", linewidth = 0.05),
    panel.grid.major = element_line(color = "gray65", linewidth = 0.05),
    
    # Legend elements
    legend.position = "top",
    legend.margin = margin(t = 5, b = 15),
    legend.background = element_rect(fill = NA, color = NA),
    legend.key = element_rect(fill = NA, color = NA),
    legend.title = element_text(size = rel(0.71), face = "bold"),
    legend.text = element_text(size = rel(0.64)),
    
    # Plot margins 
    plot.margin = margin(t = 10, r = 20, b = 10, l = 20),
  )
)

# Set theme
theme_set(weekly_theme)

6. Plot

Show code
### |-  Plot ----
p <- ggplot(team_variability, 
       aes(x = win_pct, y = margin_sd)) +
  # Geoms
  geom_hline(
    yintercept = median(team_variability$margin_sd), 
    linetype = "dashed", color = "gray70", size = 0.5
    ) +
  geom_vline(
    xintercept = 0.5, linetype = "dashed", 
    color = "gray70", size = 0.5
    ) +
  geom_point(
    aes(size = blowout_risk, color = avg_point_diff), 
    alpha = 0.9, stroke = 0.5
    ) +
  geom_text_repel(
    aes(label = team_abbr, color = avg_point_diff),
    size = 3.5, fontface = "bold", box.padding = 0.35,
    min.segment.length = 0.2, segment.alpha = 0.5,
    force = 1, max.overlaps = 15, seed = 123
    ) +
  # Annotate
  annotate(
    "text", x = 0.25, y = max(team_variability$margin_sd) - 1.3, 
    label = "HIGH RISK / UNPREDICTABLE", color = colors$palette[1], fontface = "bold", size = 4
    ) +
  annotate(
    "text", x = 0.75, y = max(team_variability$margin_sd) - 1.3, 
    label = "GOOD BUT UNPREDICTABLE", color = colors$palette[2], fontface = "bold", size = 4
    ) +
  annotate(
    "text", x = 0.25, y = median(team_variability$margin_sd) - 0.5, 
    label = "CONSISTENTLY POOR", color = colors$palette[3], fontface = "bold", size = 4
    ) +
  annotate(
    "text", x = 0.75, y = median(team_variability$margin_sd) - 0.5, 
    label = "CONSISTENTLY GOOD", color = colors$palette[4], fontface = "bold", size = 4
    ) +
  annotate(
    "text", x = 0.3, y = 11.5, 
    label = "Circle size represents likelihood of 10+ point loss", 
    color = "#333333", size = 3, fontface = "italic"
    ) +
  # Scales
  scale_color_gradient2(
    low = colors$palette[5], mid = colors$palette[6], high = colors$palette[7],
    midpoint = 0, 
    name = "Average Point Differential",
    breaks = c(-10, -5, 0, 5, 10),
    labels = c("-10", "-5", "0", "+5", "+10"),
    guide = guide_colorbar(
      title.position = "top",
      title.hjust = 0.5,
      barwidth = 12,
      barheight = 0.7,
      frame.colour = NA,
      ticks.colour = "black"
    )
  ) +
  scale_size_continuous(
    range = c(2.5, 7),
    guide = "none"
  ) +
  scale_x_continuous(
    labels = percent_format(accuracy = 1), 
    limits = c(0.15, 0.85),
    breaks = seq(0.2, 0.8, by = 0.1)
  ) +
  scale_y_continuous(
    limits = c(min(team_variability$margin_sd) * 0.95, max(team_variability$margin_sd) * 1.05)
  ) +
  # Labs
  labs(
    title = title_text,
    subtitle = subtitle_text,
    caption = caption_text,
    x = "Win Percentage",
    y = "Performance Uncertainty\n(Point Differential Standard Deviation)",
  ) +
  # Theme
  theme(
    plot.title = element_text(
      size = rel(1.8),
      family = fonts$title,
      face = "bold",
      color = colors$title,
      margin = margin(t = 5, b = 5)
    ),
    plot.subtitle = element_markdown(
      size = rel(0.9),
      family = fonts$subtitle,
      color = colors$subtitle,
      lineheight = 1.1,
      margin = margin(t = 5, b = 15)
    ),
    plot.caption = element_markdown(
      size = rel(0.55),
      family = fonts$caption,
      color = colors$caption,
      lineheight = 0.65,
      hjust = 0.5,
      halign = 0.5,
      margin = margin(t = 10, b = 5)
    ),
  )

7. Save

Show code
### |-  plot image ----  
save_plot(
  p, 
  type = "30daychartchallenge", 
  year = 2025, 
  day = 25, 
  width = 8, 
  height = 8
  )

8. Session Info

R version 4.4.1 (2024-06-14 ucrt)
Platform: x86_64-w64-mingw32/x64
Running under: Windows 11 x64 (build 22631)

Matrix products: default


locale:
[1] LC_COLLATE=English_United States.utf8 
[2] LC_CTYPE=English_United States.utf8   
[3] LC_MONETARY=English_United States.utf8
[4] LC_NUMERIC=C                          
[5] LC_TIME=English_United States.utf8    

time zone: America/New_York
tzcode source: internal

attached base packages:
[1] stats     graphics  grDevices datasets  utils     methods   base     

other attached packages:
 [1] here_1.0.1      camcorder_0.1.0 hoopR_2.1.0     ggrepel_0.9.6  
 [5] scales_1.3.0    skimr_2.1.5     janitor_2.2.0   showtext_0.9-7 
 [9] showtextdb_3.0  sysfonts_0.8.9  ggtext_0.1.2    lubridate_1.9.3
[13] forcats_1.0.0   stringr_1.5.1   dplyr_1.1.4     purrr_1.0.2    
[17] readr_2.1.5     tidyr_1.3.1     tibble_3.2.1    ggplot2_3.5.1  
[21] tidyverse_2.0.0

loaded via a namespace (and not attached):
 [1] tidyselect_1.2.1    farver_2.1.2        fastmap_1.2.0      
 [4] pacman_0.5.1        promises_1.3.0      digest_0.6.37      
 [7] timechange_0.3.0    lifecycle_1.0.4     rsvg_2.6.1         
[10] processx_3.8.4      magrittr_2.0.3      compiler_4.4.0     
[13] rlang_1.1.6         tools_4.4.0         utf8_1.2.4         
[16] yaml_2.3.10         data.table_1.16.2   knitr_1.49         
[19] labeling_0.4.3      htmlwidgets_1.6.4   curl_6.0.0         
[22] xml2_1.3.6          repr_1.1.7          websocket_1.4.2    
[25] withr_3.0.2         grid_4.4.0          fansi_1.0.6        
[28] colorspace_2.1-1    future_1.34.0       globals_0.16.3     
[31] cli_3.6.4           rmarkdown_2.29      ragg_1.3.3         
[34] generics_0.1.3      RcppParallel_5.1.10 rstudioapi_0.17.1  
[37] httr_1.4.7          tzdb_0.5.0          commonmark_1.9.2   
[40] chromote_0.4.0      rvest_1.0.4         parallel_4.4.0     
[43] base64enc_0.1-3     vctrs_0.6.5         jsonlite_1.8.9     
[46] hms_1.1.3           listenv_0.9.1       systemfonts_1.1.0  
[49] magick_2.8.5        glue_1.8.0          parallelly_1.43.0  
[52] gifski_1.32.0-1     codetools_0.2-20    ps_1.8.1           
[55] stringi_1.8.4       gtable_0.3.6        later_1.3.2        
[58] munsell_0.5.1       furrr_0.3.1         pillar_1.9.0       
[61] htmltools_0.5.8.1   R6_2.5.1            textshaping_0.4.0  
[64] rprojroot_2.0.4     evaluate_1.0.1      markdown_1.13      
[67] gridtext_0.1.5      snakecase_0.11.1    renv_1.0.3         
[70] Rcpp_1.0.13-1       svglite_2.1.3       xfun_0.49          
[73] pkgconfig_2.0.3

9. GitHub Repository

The complete code for this analysis is available in 30dcc_2025_25.qmd.

For the full repository, click here.

10. References

  1. Data Sources:
    • ESPN via { hoopR } package: hoopR
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